Probably to support racism. Like the black people crime statistics.
By using unrelated data to prove a point.
Or misrepresenting data.
For example, if your country has a 10% crime rate. Meaning 10% of the population will commit a crime at some point. Due to worker immigration the country gains 20% more people. The it is expected that of those workers about 10% will commit a crime. Thus increasing the total amount of crimes committed in the country but the crime rate is still at 10%.
Now misrepresenting would be to cry out that the workers are bad because the amount of crime has gone up.
96.3 % of all statistics are misleading.
99.7% of general questions about statistics have this same joke at least once in the replies
correlation and causation. even useless stats comparing apples and oranges, the numbers generated are only as good as the study design and methods.
By training an algorithm that will have an impact on said statistics. Not only the algorithm can cheat (see Goodhartโs law), but it can repeat biases that led to these statistics (like those law enforcement algorithms that became racists)
My favorite was the one they were training to detect cancer in imaging scans but they forgot to edit out the info stamp in the corner so it just started flagging all the scans from the cancer center!
You are describing Google Ads right now. Algorithms are better and better in reaching to poeple that are already on the purchase patch. Itโs like giving a restaurant flayers to people that are waiting for a weiter to show them a table.
Arenโt our ads amazing? Look, almost everyone who saw them made the purchase!
Analytics that ignores Goodharts law ruin everything. Movies, HR, Marketing (not much to ruin left, but you get the point), performancet review, recommendationsโฆ
